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Anal Chem. 2018 Aug 7;90(15):9068-9076. doi: 10.1021/acs.analchem.8b01388. Epub 2018 Jul 19.

Highly Accurate Detection and Identification Methodology of Xenobiotic Metabolites Using Stable Isotope Labeling, Data Mining Techniques, and Time-Dependent Profiling Based on LC/HRMS/MS.

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Department of Biotechnology, Graduate School of Engineering , Osaka University , 2-1 Yamadaoka , Suita, Osaka 565-0871 , Japan.
Division of Metabolomics , Medical Institute of Bioregulation, Kyushu University , 3-1-1 Maidashi , Higashi-ku, Fukuoka 812-8582 , Japan.
Health & Crop Sciences Research Laboratory , Sumitomo Chemical Co., Ltd. , 4-2-1 Takatsukasa , Takarazuka, Hyogo 665-8555 , Japan.
Division of Applied Microbial Technology, Graduate School of Engineering , Sojo University , 4-22-1 Ikeda , Nishi-ku, Kumamoto 860-0082 , Japan.


A generally applicable method to discover xenobiotic metabolites is important to safely and effectively develop xenobiotics. We propose an advanced method to detect and identify comprehensive xenobiotic metabolites using stable isotope labeling, liquid chromatography coupled with benchtop quadrupole Orbitrap high-resolution tandem mass spectrometry (LC/HRMS/MS), data mining techniques (alignment, peak picking, and paired-peaks filtering), in silico metabolism prediction, and time-dependent profiling. The LC/HRMS analysis was carried out using Arabidopsis T87 cultured cells treated with unlabeled or with 13C- or 2H-labeled 2,4-dichlorophenoxyacetic acid (2,4-D). Paired-peak filtering enabled the accurate detection of 83 candidates for 2,4-D metabolites without any false positive peaks derived from solvents or the biological matrix. We confirmed 10 previously reported 2,4-D metabolites and identified 16 novel 2,4-D metabolites. Our method provides accurate detection and identification of comprehensive xenobiotic metabolites and represents a potentially useful tool for elucidating xenobiotic metabolism.

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